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Title 

Anomaly intrusion detection based on hyper-ellipsoid in the kernel feature space

Authors 

H LeeD MoonI KimHo Seok JungD Park

Issue Date 

2015

Citation 

KSII Transactions on Internet and Information System (한국인터넷정보학회), vol. 9, no. 3, pp. 1173-1192

Keywords 

Anomaly detectionIntrusion detectionKernel principal component analysisMinimum enclosing ellipsoid

Abstract 

The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.

ISSN 

1976-7277

Link 

http://dx.doi.org/10.3837/tiis.2015.03.019

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2019-05-02


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